Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

• Published in: Biomedical engineering online Vol. 16; no. 1; p. 5
• Main Authors: Chai, Rifai, Naik, Ganesh R., Ling, Sai Ho, Nguyen, Hung T.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.01.2017; BioMed Central Ltd; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biomedical Research - instrumentation; Biotechnology; Brain - physiology; Brain - physiopathology; Brain-Computer Interfaces; Electroencephalography; Electroencephalography - instrumentation; Electrophysiological Phenomena; Engineering; Female; Human-computer interaction; Humans; Male; Middle Aged; Spinal Cord Injuries - physiopathology; Technology application; Wireless Technology; Electroencephalography; Artificial neural network; Embedded system; Cyber physical system; Brain–computer interface; Hybrid system
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-016-0303-x

Background One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.